{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T14:30:57Z","timestamp":1783607457652,"version":"3.55.0"},"reference-count":61,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,19]],"date-time":"2020-03-19T00:00:00Z","timestamp":1584576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["LUFTHY \u2013 3160384700"],"award-info":[{"award-number":["LUFTHY \u2013 3160384700"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10 and PN10) in a street canyon in M\u00fcnster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO2, NOx, and O3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.<\/jats:p>","DOI":"10.3390\/ijerph17062025","type":"journal-article","created":{"date-parts":[[2020,3,19]],"date-time":"2020-03-19T10:01:35Z","timestamp":1584612095000},"page":"2025","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables"],"prefix":"10.3390","volume":"17","author":[{"given":"Laura","family":"Goulier","sequence":"first","affiliation":[{"name":"Climatology Research Group, University of M\u00fcnster, Heisenbergstra\u00dfe 2, 48149 M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1941-1569","authenticated-orcid":false,"given":"Bastian","family":"Paas","sequence":"additional","affiliation":[{"name":"Climatology Research Group, University of M\u00fcnster, Heisenbergstra\u00dfe 2, 48149 M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Ehrnsperger","sequence":"additional","affiliation":[{"name":"Climatology Research Group, University of M\u00fcnster, Heisenbergstra\u00dfe 2, 48149 M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3502-6721","authenticated-orcid":false,"given":"Otto","family":"Klemm","sequence":"additional","affiliation":[{"name":"Climatology Research Group, University of M\u00fcnster, Heisenbergstra\u00dfe 2, 48149 M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1097\/EDE.0b013e318276ccb8","article-title":"Long-term exposure to outdoor air pollution and incidence of cardiovascular diseases","volume":"24","author":"Atkinson","year":"2013","journal-title":"Epidemiology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.ijheh.2007.07.012","article-title":"Fine particles, a major threat to children","volume":"210","author":"Heinrich","year":"2007","journal-title":"Int. J. Hyg. Environ. Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s11869-014-0265-8","article-title":"Traffic-related pollution and asthma prevalence in children. Quantification of associations with nitrogen dioxide","volume":"7","author":"Favarato","year":"2014","journal-title":"Air Qual. Atmos. Health"},{"key":"ref_4","unstructured":"Vallero, D.A. (2014). Fundamentals of Air Pollution, Elsevier, Academic Press. [5th ed.]."},{"key":"ref_5","unstructured":"(2019, December 13). Criteria Air Pollutants, Available online: https:\/\/www.epa.gov\/criteria-air-pollutants#self."},{"key":"ref_6","unstructured":"Richtlinie 2008\/50\/EG DES EUROP\u00c4ISCHEN PARLAMENTS UND DES RATES vom 21 (2019, December 20). Mai 2008 \u00fcber Luftqualit\u00e4t und saubere Luft f\u00fcr Europa: Amtsblatt der Europ\u00e4ischen Union. Available online: https:\/\/eur-lex.europa.eu\/legal-content\/DE\/TXT\/PDF\/?uri=CELEX:32008L0050&from=DE."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.atmosenv.2014.07.050","article-title":"Ammonia exhaust emissions from spark ignition vehicles over the New European Driving Cycle","volume":"97","author":"Zardini","year":"2014","journal-title":"Atmos. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10874-007-9074-x","article-title":"Role of atmospheric ammonia in the formation of inorganic secondary particulate matter: A study at Kanpur, India","volume":"58","author":"Sharma","year":"2007","journal-title":"J. Atmos. Chem."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8092","DOI":"10.1007\/s11356-013-2051-9","article-title":"Ammonia in the atmosphere: A review on emission sources, atmospheric chemistry and deposition on terrestrial bodies","volume":"20","author":"Behera","year":"2013","journal-title":"Environ. Sci. Pollut. Res. Int."},{"key":"ref_10","unstructured":"(2019, December 11). Sector Share of Nitrogen Oxides Emissions. Available online: https:\/\/www.eea.europa.eu\/data-and-maps\/daviz\/sector-share-of-nitrogen-oxides-emissions#tab-chart_1."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.jaerosci.2008.05.007","article-title":"Source apportionment of particulate matter in Europe: A review of methods and results","volume":"39","author":"Viana","year":"2008","journal-title":"J. Aerosol Sci."},{"key":"ref_12","unstructured":"(2019, December 11). Circulatory Diseases. Available online: https:\/\/www.umweltbundesamt.de\/en\/topics\/transport-noise\/noise-effects\/circulatory-diseases."},{"key":"ref_13","unstructured":"(2019, December 11). Traffic Noise. Available online: https:\/\/www.umweltbundesamt.de\/en\/topics\/transport-noise\/traffic-noise."},{"key":"ref_14","unstructured":"World Health Organisation Regional Office for Europe, and European Commission (2011). Burden of Disease from Environmental Noise, Quantification of Healthy Life Years Lost in Europe."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1127\/metz\/2016\/0707","article-title":"Regional and local effects of electric vehicles on air quality and noise","volume":"25","author":"Wurzler","year":"2016","journal-title":"METZ"},{"key":"ref_16","unstructured":"Gloaguen, J.-R., Can, A., Lagrange, M., and Petiot, J.-F. (2016, October 03). Estimating traffic noise levels using acoustic monitoring: A preliminary study. Available online: https:\/\/hal.archives-ouvertes.fr\/hal-01375796."},{"key":"ref_17","unstructured":"Dekokinick, L., Botteldooren, D., de Coensel, B., and Intpanis, L. (2016, January 21\u201324). Spectral noise measurements supply instantaneous traffic information for multidisciplinary mobility and traffic related projects. Proceedings of the Internoise 2016\u2014Towards a Quiter Future, Hamburg, Germany."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/environments4020026","article-title":"Modelling of Urban Near-Road Atmospheric PM Concentrations Using an Artificial Neural Network Approach with Acoustic Data Input","volume":"4","author":"Paas","year":"2017","journal-title":"Environments"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4777","DOI":"10.1007\/s11356-012-1451-6","article-title":"Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations","volume":"20","author":"Arhami","year":"2013","journal-title":"Environ. Sci. Pollut. Res. Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.trd.2008.10.004","article-title":"Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach","volume":"14","author":"Cai","year":"2009","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1175\/WAF980.1","article-title":"Application of artificial neural network forecasts to predict fog at canberra international airport","volume":"22","author":"Fabbian","year":"2007","journal-title":"Weather Forecast."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pasero, E., and Mesin, L. (2010). Artificial Neural Networks for Pollution Forecast. Air Pollut. IntechOpen.","DOI":"10.5772\/10050"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.jenvman.2015.12.012","article-title":"Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models","volume":"168","author":"Adams","year":"2016","journal-title":"J. Environ. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gardner, M.W., and Dorling, S.R. (1998). Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmos. Environ., 709\u2013719.","DOI":"10.1016\/S1352-2310(98)00230-1"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rey, G.D., and Wender, K.F. (2018). Neuronale Netze. Eine Einf\u00fchrung in die Grundlagen, Anwendungen und Datenauswertung, Hogrefe. [3rd ed.].","DOI":"10.1024\/85796-000"},{"key":"ref_26","first-page":"30","article-title":"Artificial neural network applications in air quality monitoring and management","volume":"2","author":"Prachi","year":"2011","journal-title":"Int. J. Environ. Rehabil. Conserv."},{"key":"ref_27","unstructured":"(2020, February 10). Guidance Document on Modelling Quality Objectives and Benchmarking. Available online: https:\/\/fairmode.jrc.ec.europa.eu\/document\/fairmode\/WG1\/Guidance_MQO_Bench_vs3.1.1.pdf."},{"key":"ref_28","unstructured":"LI-COR, Inc. (2016). EddyPro Software 6.2.0, LI-COR, Inc."},{"key":"ref_29","unstructured":"(2019, December 18). Continuous Emissions Monitoring Systems (CEMS) From A-Z: A Multi-Part Series - NOx Analyzer (Part 6). Available online: https:\/\/www.monsol.com\/news\/post\/continuous-emissions-monitoring-systems-cems-from-a-z-a-multi-part-series-nox-analyzer-part-6."},{"key":"ref_30","unstructured":"(2019, December 18). Umgebungsl\u00e4rm NRW. Available online: https:\/\/www.umgebungslaerm-kartierung.nrw.de\/."},{"key":"ref_31","unstructured":"NTI Audio Specifications (2019, December 14). Technical Data XL2. Available online: https:\/\/www.nti-audio.com\/Portals\/0\/data\/en\/XL2-Specifications.pdf."},{"key":"ref_32","unstructured":"RStudio, Inc. (2018). RStudio 3.6.1: Integrated Development for R., RStudio, Inc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.envsoft.2019.06.014","article-title":"A review of artificial neural network models for ambient air pollution prediction","volume":"119","author":"Cabaneros","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_34","unstructured":"Patterson, J., and Gibson, A. (2017). Deep Learning. A Practitioner\u2019s Approach, O\u2019Reilly Media. [1st ed.]."},{"key":"ref_35","unstructured":"Goodfellow, I., Courville, A., and Bengio, Y. (2018). Deep Learning. Das Umfassende Handbuch: Grundlagen, Aktuelle Verfahren und Algorithmen, Neue Forschungsans\u00e4tze, Verlags GmbH & Co. KG. [1st ed.]."},{"key":"ref_36","unstructured":"(2019, December 08). Introduction to FeedForward Neural Networks. Available online: https:\/\/towardsdatascience.com\/feed-forward-neural-networks-c503faa46620."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dijkstra, H.A. (2019). Networks in Climate, Cambridge University Press.","DOI":"10.1017\/9781316275757"},{"key":"ref_38","unstructured":"Rashid, T. (2017). Neuronale Netze Selbst Programmieren. Ein Verst\u00e4ndlicher Einstieg Mit Python, O\u2019Reilly. [1st ed.]."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rojas, R. (1996). Neural Networks. A Systematic Introduction, Springer.","DOI":"10.1007\/978-3-642-61068-4"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.neunet.2009.11.009","article-title":"Data splitting for artificial neural networks using SOM-based stratified sampling","volume":"23","author":"May","year":"2010","journal-title":"Neural Netw."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Suzuki, K. (2011). Review of Input Variable Selection. Methods for Artificial Neural Networks. Artificial Neural Networks. Methodological Advances and Biomedical Applications, IntechOpen.","DOI":"10.5772\/644"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.jhydrol.2008.10.019","article-title":"Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach","volume":"367","author":"Fernando","year":"2008","journal-title":"J. Hydrol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/S0022-1694(00)00346-2","article-title":"Seasonal to interannual rainfall probabilistic forecasts for improvedwater supply management: Part 1\u2014A strategy for systempredictor identification","volume":"239","author":"Sharma","year":"2000","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.envsoft.2008.03.007","article-title":"Non-linear variable selection for artificial neural networks using partial mutual information","volume":"23","author":"May","year":"2008","journal-title":"Environ. Model. Softw."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jhydrol.2004.06.021","article-title":"Input determination for neural network models in water resources applications. Part 1\u2014Background and methodology","volume":"301","author":"Bowden","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A NewLook at the Statistical Model Identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_47","unstructured":"Reitermanov\u00e1, Z. (2010). Data Splitting. WDS\u201910 Proceedings of Contributed Papers, MatfyzPress. Part 1."},{"key":"ref_48","unstructured":"Wu, W., May, R.J., Dandy, G.C., and Maier, H.R. (2012, January 1\u20135). A method for comparing data splitting approaches for developing hydrological ANN models. Proceedings of the 6th International Congress on Environmental Modelling and Software (iEMSs), Leipzig, Germany."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/neco.1992.4.1.1","article-title":"Neural Networks and the Bias\/Variance Dilemma","volume":"4","author":"Geman","year":"1992","journal-title":"Neural Comput."},{"key":"ref_50","unstructured":"Hagan, M.T., Demuth, H.B., Hudson Beale, M., and de Jes\u00fas, O. (1995). Neural Network Design, Martin T. Hagan. [2nd ed.]."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1109\/5.58325","article-title":"The self-organizing map","volume":"78","author":"Kohonen","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_52","unstructured":"Wilks, D.S. (2009). Statistical Methods in the Atmospheric Sciences, Elsevier. [2nd ed.]."},{"key":"ref_53","unstructured":"Seinfeld, J.H., and Pandis, S.N. (2006). Atmospheric Chemistry and Physics. From Air Pollution to Climate Change, Wiley. [2nd ed.]."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.apr.2016.01.004","article-title":"Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions","volume":"7","author":"Bai","year":"2016","journal-title":"Atmos. Pollut. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"9370","DOI":"10.1016\/j.atmosenv.2007.09.005","article-title":"Estimation and validation of PM2.5\/PM10 exhaust and non-exhaust emission factors for practical street pollution modelling","volume":"41","author":"Ketzel","year":"2007","journal-title":"Atmos. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.atmosenv.2016.03.017","article-title":"Non-exhaust PM emissions from electric vehicles","volume":"134","author":"Timmers","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1016\/j.atmosenv.2004.02.014","article-title":"Simulation of NOx and ultrafine particles in a street canyon in Stockholm, Sweden","volume":"38","author":"Gidhagen","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1016\/j.atmosenv.2004.02.020","article-title":"Modelling the fate of ultrafine particles from exhaust pipe to rural background: An analysis of time scales for dilution, coagulation and deposition","volume":"38","author":"Ketzel","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.envpol.2016.05.003","article-title":"The washout effects of rainfall on atmospheric particulate pollution in two Chinese cities","volume":"215","author":"Guo","year":"2016","journal-title":"Environ. Pollut."},{"key":"ref_60","unstructured":"Zellner, R. (2011). Chemie \u00fcber den Wolken. \u2026 und Darunter, Wiley-VCH Verlag GmbH & Co. KGaA. [1st ed.]."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1021\/ed078p1641","article-title":"Local and regional ozone: A student study project","volume":"78","author":"Klemm","year":"2001","journal-title":"J. Chem. Educ."}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/17\/6\/2025\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:09:41Z","timestamp":1760173781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/17\/6\/2025"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,19]]},"references-count":61,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["ijerph17062025"],"URL":"https:\/\/doi.org\/10.3390\/ijerph17062025","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,19]]}}}